English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Most previous work on grammar induction focuses on learning phrasal ordependency structure purely from text. However, because the signal provided bytext alone is limited, recently introduced visually grounded syntax models makeuse of multimodal information leading to improved performance in constituencygrammar induction. However, as compared to dependency grammars, constituencygrammars do not provide a straightforward way to incorporate visual informationwithout enforcing language-specific heuristics. In this paper, we propose anunsupervised grammar induction model that leverages word concreteness and astructural vision-based heuristic to jointly learn constituency-structure anddependency-structure grammars. Our experiments find that concreteness is astrong indicator for learning dependency grammars, improving the directattachment score (DAS) by over 50\% as compared to state-of-the-art modelstrained on pure text. Next, we propose an extension of our model that leveragesboth word concreteness and visual semantic role labels in constituency anddependency parsing. Our experiments show that the proposed extensionoutperforms the current state-of-the-art visually grounded models inconstituency parsing even with a smaller grammar size.

Dependency Induction Through the Lens of Visual Perception